import argparse
import json
import os
from copy import deepcopy
from pathlib import Path
from threading import Thread

import cv2
import numpy as np
import torch
import yaml
from tqdm import tqdm

from models.experimental import attempt_load
from pts_utils import iou_polygenMN
from utils.datasets import create_dataloader
from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, check_requirements, \
    box_iou, non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, increment_path, colorstr
from utils.loss import get_roi_feat_map, affine6to8pts, visualise_sigmoid_prob_map, decode_affine_net
from utils.metrics import ap_per_class, ConfusionMatrix
from utils.plots import plot_images, output_to_target, plot_study_txt
from utils.torch_utils import select_device, time_synchronized, TracedModel, is_parallel


class lpSta:
    tp = 0
    fp = 0
    num_targets = 0
    Precision = 0
    Recall = 0
    ious = None

    best_map = 0.0
    best_m_pts = 0.0
    best_p_pts = 0.0
    best_r_pts = 0.0

    def __init__(self):
        pass

    @staticmethod
    def reset():
        lpSta.fp = 0
        lpSta.tp = 0
        lpSta.num_targets = 0
        lpSta.Precision = 0
        lpSta.Recall = 0
        lpSta.ious = None

    @staticmethod
    def stat():
        lpSta.Precision = lpSta.tp / (lpSta.tp + lpSta.fp + 1e-5)
        lpSta.Recall = lpSta.tp / (lpSta.num_targets + 1e-5)

    @staticmethod
    def save(model, epoch, map, m_pts, p_pts, r_pts):
        do_save = False
        if map > lpSta.best_map:
            lpSta.best_map = map
            do_save = True
        if m_pts > lpSta.best_m_pts:
            lpSta.best_m_pts = m_pts
            do_save = True
        if p_pts > lpSta.best_p_pts:
            lpSta.best_p_pts = p_pts
            do_save = True
        if r_pts > lpSta.best_r_pts:
            lpSta.best_r_pts = r_pts
            do_save = True

        if do_save and map > 0.3:
            ckpt = {'epoch': epoch,
                    'best_fitness': 0.0,
                    'training_results': "None",
                    'model': deepcopy(model.module if is_parallel(model) else model).half(),
                    'ema': None,
                    'updates': None,
                    'optimizer': None,
                    'wandb_id': None}

            # Save last, best and delete
            # torch.save(ckpt, last)
            torch.save(ckpt,
                       "/project/train/models/e{}_{:.2f}_m{:.2f}_p{:.2f}_r{:.2f}.pt"
                       .format(epoch, map, m_pts, p_pts, r_pts))


def pts_add_box_center(boxes: torch.tensor, ptses: torch.tensor):
    # box format x1, y1, x2 , y2
    ptses_decoded = torch.zeros_like(ptses, device=boxes.device)
    for i, (box, pts) in enumerate(zip(boxes, ptses)):
        x1, y1, x2, y2 = box[0:4]
        w, h = x2 - x1, y2 - y1
        xc = (x1 + x2) / 2
        yc = (y1 + y2) / 2
        pts_decoded = pts * torch.tensor([w, h, w, h, w, h, w, h], device=pts.device).reshape([1, 8])
        pts_decoded = pts_decoded + torch.tensor([x1, y1, x1, y1, x1, y1, x1, y1], device=pts.device).reshape([1, 8])
        ptses_decoded[i] = pts_decoded
    return ptses_decoded


def xyxy2xywh_relative(rects_ny, w, h, featDim=640):
    scale = featDim / max(w, h)
    rects_ny = rects_ny * scale
    gap_small_side = (abs(w - h) * scale) / 2
    rects_ny[:, 1] += gap_small_side
    rects_ny[:, 3] += gap_small_side

    relative_xyxy = rects_ny / np.array([featDim, featDim, featDim, featDim]).reshape([-1, 4])
    wh = relative_xyxy[:, 2:4] - relative_xyxy[:, 0:2]

    relative_xywh = relative_xyxy.copy()
    relative_xywh[:, 0:2] += wh / 2
    relative_xywh[:, 2:4] = wh
    return relative_xywh


def use_box_2_affine_decode(pts_feats, rects_ny, idx, w, h, model):
    relative_xywh = xyxy2xywh_relative(rects_ny, w, h)
    num_boxes = rects_ny.shape[0]
    pts_feats_for_batch_idx = [pts_feats[0][idx:idx + 1, ...].float().repeat([num_boxes, 1, 1, 1]),
                               pts_feats[1][idx:idx + 1, ...].float().repeat([num_boxes, 1, 1, 1]),
                               pts_feats[2][idx:idx + 1, ...].float().repeat([num_boxes, 1, 1, 1]),
                               ]

    roi_feats_for_batch_idx = get_roi_feat_map(torch.tensor(relative_xywh,
                                                            device=pts_feats_for_batch_idx[0].device,
                                                            dtype=torch.float32),
                                               pts_feats_for_batch_idx)
    pts_pre_feat_maps = model(roi_feats_for_batch_idx.half())
    decoded_ptses = decode_affine_net(pts_pre_feat_maps, rects_ny, name="TestDecode")
    return decoded_ptses


def draw_pts_at_im_tensor(img_raw, rects, pts, idx, ratio_pad, confidences, winname="im", time=1, conf_thresh=0.3,
                          pts_feats=None, model=None):
    # pts (N,8) tensor
    canvas = img_raw[idx].permute([1, 2, 0]).numpy()
    if pts is not None:
        pts_ny = pts.reshape(-1, 4, 2).detach().cpu().numpy().astype(np.int32)
    rects_ny = rects.reshape(-1, 4).detach().cpu().numpy().astype(np.int32)

    conf_thresh = confidences.max() * 0.7

    if rects_ny.shape[0] > 100:
        pass
    else:
        gain = ratio_pad[0][0]
        pad = ratio_pad[1]
        pad = list(np.array(pad).astype(np.int32))
        h, w, _ = canvas.shape
        canvas = canvas[pad[1]:h - pad[1], pad[0]:w - pad[0], :]
        h, w, _ = canvas.shape
        canvas = cv2.resize(canvas, (int(w / gain), int(h / gain)))
        canvas_h, canvas_w, canvas_c = canvas.shape

        for i, rect in enumerate(rects_ny):
            # xc, yc, w, h = rect
            # x1, y1, x2, y2 = int(xc - w / 2), int(yc - h / 2), int(xc + w / 2), int(yc + h / 2)
            conf = float(confidences[i])
            if conf < conf_thresh:
                continue
            x1, y1, x2, y2 = rect
            cv2.rectangle(canvas, (x1, y1), (x2, y2), (0, 0, 255), 4)
            cv2.putText(canvas, "{:.2f}".format(conf), (x1, y1), cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 255), 4)
            if pts is not None:
                if i < pts_ny.shape[0]:
                    cv2.polylines(canvas, pts_ny[i].reshape(-1, 4, 2), True, (0, 255, 255), 2)
            if pts_feats is not None:
                decoded_pts = use_box_2_affine_decode(pts_feats, rects_ny[i:i + 1, ...],
                                                      idx, canvas_w, canvas_h, model)
                cv2.polylines(canvas, decoded_pts.reshape(-1, 4, 2), True, (255, 255, 0), 4)

            if pts_feats is not None and pts is not None:
                # decoded_pts.shape[0] == pts.shape[0]
                pts_ious = iou_polygenMN(torch.from_numpy(decoded_pts).to(pts.device).reshape([-1, 8])
                                         , pts)
                if (pts_ious > 0.8).sum() > 0:
                    lpSta.tp += 1
                else:
                    lpSta.fp += 1

                ious_choosed = pts_ious[pts_ious > 0.].reshape(-1)
                if ious_choosed.shape[0] > 0:
                    lpSta.ious = ious_choosed if lpSta.ious is None else torch.cat((lpSta.ious, ious_choosed), 0)

            # if pts_feats is not None:
            #     get_roi_feat_map()

    cv2.namedWindow(winname, cv2.WINDOW_NORMAL)
    cv2.imshow(winname, canvas)
    cv2.resizeWindow(winname, 800, 600)
    cv2.waitKey(time)


def test(data,
         weights=None,
         batch_size=32,
         imgsz=640,
         conf_thres=0.001,
         iou_thres=0.6,  # for NMS
         save_json=False,
         single_cls=False,
         augment=False,
         verbose=False,
         model=None,
         dataloader=None,
         save_dir=Path(''),  # for saving images
         save_txt=False,  # for auto-labelling
         save_hybrid=False,  # for hybrid auto-labelling
         save_conf=False,  # save auto-label confidences
         plots=True,
         wandb_logger=None,
         compute_loss=None,
         half_precision=True,
         trace=False,
         is_coco=False,
         epoch=None
         ):
    # Initialize/load model and set device
    training = model is not None
    if training:  # called by train.py
        device = next(model.parameters()).device  # get model device

    else:  # called directly
        set_logging()
        device = select_device(opt.device, batch_size=batch_size)

        # Directories
        save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
        (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir

        # Load model
        model = attempt_load(weights, map_location=device)  # load FP32 model
        gs = max(int(model.stride.max()), 32)  # grid size (max stride)
        imgsz = check_img_size(imgsz, s=gs)  # check img_size

        if trace:
            model = TracedModel(model, device, opt.img_size)

    # Half
    half = device.type != 'cpu' and half_precision  # half precision only supported on CUDA
    if half:
        model.half()

    # Configure
    model.eval()
    if isinstance(data, str):
        is_coco = data.endswith('coco.yaml')
        with open(data) as f:
            data = yaml.load(f, Loader=yaml.SafeLoader)
    check_dataset(data)  # check
    nc = 1 if single_cls else int(data['nc'])  # number of classes
    iouv = torch.linspace(0.5, 0.95, 10).to(device)  # iou vector for mAP@0.5:0.95
    niou = iouv.numel()

    # Logging
    log_imgs = 0
    if wandb_logger and wandb_logger.wandb:
        log_imgs = min(wandb_logger.log_imgs, 100)
    # Dataloader
    if not training:
        if device.type != 'cpu':
            model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
        task = opt.task if opt.task in ('train', 'val', 'test') else 'val'  # path to train/val/test images
        dataloader = create_dataloader(data[task], imgsz, batch_size, gs, opt, pad=0.5, rect=True,
                                       prefix=colorstr(f'{task}: '))[0]

    seen = 0
    confusion_matrix = ConfusionMatrix(nc=nc)
    names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
    coco91class = coco80_to_coco91_class()
    s = ('%20s' + '%12s' * 9) % (
        'Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95', 'P-pts', 'R-pts', 'M-pts')
    p, r, f1, mp, mr, map50, map, t0, t1, p_pts, r_pts, m_pts = 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
    loss = torch.zeros(3, device=device)
    jdict, stats, ap, ap_class, wandb_images = [], [], [], [], []
    lpSta.reset()
    for batch_i, (img, targets, targets_pts, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
        img_raw = img.clone()
        img = img.to(device, non_blocking=True)
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        targets = targets.to(device)
        nb, _, height, width = img.shape  # batch size, channels, height, width

        with torch.no_grad():
            # Run model
            t = time_synchronized()
            out, pts_feat, x_feat = model(img, augment=augment)  # inference and training outputs
            t0 += time_synchronized() - t

            # Compute loss
            if compute_loss:
                loss += compute_loss([x.float() for x in x_feat], targets)[1][:3]  # box, obj, cls

            # Run NMS
            targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device)  # to pixels
            lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else []  # for autolabelling
            t = time_synchronized()
            # out, _ = non_max_suppression(out, None, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb,
            #                                    multi_label=True)
            out, _ = non_max_suppression(out, None, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb,
                                         multi_label=True)
            t1 += time_synchronized() - t

        # Statistics per image
        for si, (pred) in enumerate(out):
            labels = targets[targets[:, 0] == si, 1:]
            labels_pts = targets_pts[targets_pts[:, 0] == si, 1:]
            nl = len(labels)
            tcls = labels[:, 0].tolist() if nl else []  # target class
            path = Path(paths[si])
            seen += 1

            if len(pred) == 0:
                if nl:
                    stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
                continue

            # Predictions: x1, y1 , x2, y2 , prob , clsID
            predn = pred.clone()
            scale_coords(img[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1])  # native-space pred

            # Append to text file
            if save_txt:
                gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]]  # normalization gain whwh
                for *xyxy, conf, cls in predn.tolist():
                    xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                    line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
                    with open(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f:
                        f.write(('%g ' * len(line)).rstrip() % line + '\n')

            # W&B logging - Media Panel Plots
            if len(wandb_images) < log_imgs and wandb_logger.current_epoch > 0:  # Check for test operation
                if wandb_logger.current_epoch % wandb_logger.bbox_interval == 0:
                    box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
                                 "class_id": int(cls),
                                 "box_caption": "%s %.3f" % (names[cls], conf),
                                 "scores": {"class_score": conf},
                                 "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
                    boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
                    wandb_images.append(wandb_logger.wandb.Image(img[si], boxes=boxes, caption=path.name))
            wandb_logger.log_training_progress(predn, path, names) if wandb_logger and wandb_logger.wandb_run else None

            # Append to pycocotools JSON dictionary
            if save_json:
                # [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
                image_id = int(path.stem) if path.stem.isnumeric() else path.stem
                box = xyxy2xywh(predn[:, :4])  # xywh
                box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
                for p, b in zip(pred.tolist(), box.tolist()):
                    jdict.append({'image_id': image_id,
                                  'category_id': coco91class[int(p[5])] if is_coco else int(p[5]),
                                  'bbox': [round(x, 3) for x in b],
                                  'score': round(p[4], 5)})

            # Assign all predictions as incorrect
            correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device)
            if nl:
                detected = []  # target indices
                tcls_tensor = labels[:, 0]

                # target boxes
                tbox = xywh2xyxy(labels[:, 1:5])
                scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1])  # native-space labels
                tbox_pts = pts_add_box_center(tbox, labels_pts)

                if plots:
                    confusion_matrix.process_batch(predn, torch.cat((labels[:, 0:1], tbox), 1))

                # Per target class
                for cls in torch.unique(tcls_tensor):
                    ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1)  # prediction indices
                    pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1)  # target indices

                    # Search for detections
                    if pi.shape[0]:
                        # Prediction to target ious
                        ious, i = box_iou(predn[pi, :4], tbox[ti]).max(1)  # best ious, indices
                        # ious_pts, i_pts = iou_polygenMN(predn_pts[pi], tbox_pts[ti]).max(1)  # best ious pts, indices

                        if 1 == cls:
                            draw_pts_at_im_tensor(img_raw, tbox[ti], tbox_pts[ti], si, shapes[si][1]
                                                  , torch.ones_like(tbox[ti, 0]), winname="true", time=1)
                            # draw_pts_at_im_tensor(img_raw, predn[pi, :4], None, si, shapes[si][1]
                            #                       , predn[pi, 4], winname="pred", time=1, pts_feats=pts_feat,
                            #                       model=model.model[125].affineNet)
                            draw_pts_at_im_tensor(img_raw, predn[pi, :4], tbox_pts[ti], si, shapes[si][1]
                                                  , predn[pi, 4], winname="pred", time=1,
                                                  pts_feats=pts_feat,
                                                  model=model.model[125].affineNet)
                            lpSta.num_targets += tbox_pts[ti].shape[0]
                        # Append detections
                        detected_set = set()
                        for j in (ious > iouv[0]).nonzero(as_tuple=False):
                            d = ti[i[j]]  # detected target
                            if d.item() not in detected_set:
                                detected_set.add(d.item())
                                detected.append(d)
                                correct[pi[j]] = ious[j] > iouv  # iou_thres is 1xn
                                if len(detected) == nl:  # all targets already located in image
                                    break

            # Append statistics (correct, conf, pcls, tcls)
            stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))

        # Plot images
        if plots and batch_i < 3:
            f = save_dir / f'test_batch{batch_i}_labels.jpg'  # labels
            Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start()
            f = save_dir / f'test_batch{batch_i}_pred.jpg'  # predictions
            Thread(target=plot_images, args=(img, output_to_target(out), paths, f, names), daemon=True).start()
    # stats correct flag
    # T1 score0.5 score0.55 score0.6 score0.65 ... score0.95
    # T2 score0.5 score0.55 score0.6 score0.65 ... score0.95
    # ...
    # TN score0.5 score0.55 score0.6 score0.65 ... score0.95

    # stats correct flag for pts
    # T1 PTSiou0.8 & T1score0.5
    # T2 PTSiou0.8 & T1score0.5
    # ...
    # TN PTSiou0.8 & T1score0.5

    # Compute statistics
    # example shape
    #   113,10  , correct flag , pred box num , iou from 0.5 to 0.95
    #   113 , pred confidence
    #   113 , pred cls
    #   20  , target cls , true box num classes
    stats = [np.concatenate(x, 0) for x in zip(*stats)]  # to numpy
    if len(stats) and stats[0].any():
        p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
        ap50, ap = ap[:, 0], ap.mean(1)  # AP@0.5, AP@0.5:0.95
        mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
        nt = np.bincount(stats[3].astype(np.int64), minlength=nc)  # number of targets per class
    else:
        nt = torch.zeros(1)

    lpSta.stat()
    p_pts = lpSta.Precision
    r_pts = lpSta.Recall
    m_pts = 0.0 if lpSta.ious is None else float(lpSta.ious.mean())
    # Print results
    pf = '%20s' + '%12i' * 2 + '%12.3g' * 7  # print format
    print(pf % ('all', seen, nt.sum(), mp, mr, map50, map, p_pts, r_pts, m_pts))

    lpSta.save(model, epoch, map, m_pts, p_pts, r_pts);
    # Print results per class
    if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
        for i, c in enumerate(ap_class):
            print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))

    # Print speeds
    t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size)  # tuple
    if not training:
        print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t)

    # Plots
    if plots:
        confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
        if wandb_logger and wandb_logger.wandb:
            val_batches = [wandb_logger.wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]
            wandb_logger.log({"Validation": val_batches})
    if wandb_images:
        wandb_logger.log({"Bounding Box Debugger/Images": wandb_images})

    # Save JSON
    if save_json and len(jdict):
        w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else ''  # weights
        anno_json = './coco/annotations/instances_val2017.json'  # annotations json
        pred_json = str(save_dir / f"{w}_predictions.json")  # predictions json
        print('\nEvaluating pycocotools mAP... saving %s...' % pred_json)
        with open(pred_json, 'w') as f:
            json.dump(jdict, f)

        try:  # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
            from pycocotools.coco import COCO
            from pycocotools.cocoeval import COCOeval

            anno = COCO(anno_json)  # init annotations api
            pred = anno.loadRes(pred_json)  # init predictions api
            eval = COCOeval(anno, pred, 'bbox')
            if is_coco:
                eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files]  # image IDs to evaluate
            eval.evaluate()
            eval.accumulate()
            eval.summarize()
            map, map50 = eval.stats[:2]  # update results (mAP@0.5:0.95, mAP@0.5)
        except Exception as e:
            print(f'pycocotools unable to run: {e}')

    # Return results
    model.float()  # for training
    if not training:
        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
        print(f"Results saved to {save_dir}{s}")
    maps = np.zeros(nc) + map
    for i, c in enumerate(ap_class):
        maps[c] = ap[i]
    return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t


if __name__ == '__main__':
    parser = argparse.ArgumentParser(prog='test.py')
    parser.add_argument('--weights', nargs='+', type=str, default='yolov7.pt', help='model.pt path(s)')
    parser.add_argument('--data', type=str, default='data/coco.yaml', help='*.data path')
    parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch')
    parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
    parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.65, help='IOU threshold for NMS')
    parser.add_argument('--task', default='val', help='train, val, test, speed or study')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    parser.add_argument('--verbose', action='store_true', help='report mAP by class')
    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
    parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')
    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
    parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
    parser.add_argument('--project', default='runs/test', help='save to project/name')
    parser.add_argument('--name', default='exp', help='save to project/name')
    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
    parser.add_argument('--no-trace', action='store_true', help='don`t trace model')
    opt = parser.parse_args()
    opt.save_json |= opt.data.endswith('coco.yaml')
    opt.data = check_file(opt.data)  # check file
    print(opt)
    # check_requirements()

    if opt.task in ('train', 'val', 'test'):  # run normally
        test(opt.data,
             opt.weights,
             opt.batch_size,
             opt.img_size,
             opt.conf_thres,
             opt.iou_thres,
             opt.save_json,
             opt.single_cls,
             opt.augment,
             opt.verbose,
             save_txt=opt.save_txt | opt.save_hybrid,
             save_hybrid=opt.save_hybrid,
             save_conf=opt.save_conf,
             trace=not opt.no_trace,
             )

    elif opt.task == 'speed':  # speed benchmarks
        for w in opt.weights:
            test(opt.data, w, opt.batch_size, opt.img_size, 0.25, 0.45, save_json=False, plots=False)

    elif opt.task == 'study':  # run over a range of settings and save/plot
        # python test.py --task study --data coco.yaml --iou 0.65 --weights yolov7.pt
        x = list(range(256, 1536 + 128, 128))  # x axis (image sizes)
        for w in opt.weights:
            f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt'  # filename to save to
            y = []  # y axis
            for i in x:  # img-size
                print(f'\nRunning {f} point {i}...')
                r, _, t = test(opt.data, w, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json,
                               plots=False)
                y.append(r + t)  # results and times
            np.savetxt(f, y, fmt='%10.4g')  # save
        os.system('zip -r study.zip study_*.txt')
        plot_study_txt(x=x)  # plot
